SENECA: An Attention Support Tool for Context-related Content Learning

Abstract

In this paper, we suggest SENECA, a tool that attempts to assist students who follow remote classes in maintaining/capturing attention, allowing them to focus on context-driven learning. Distance education has a number of disadvantages, including a lack of physical interaction between students and teachers, emotional and motivational isolation as a result of this strategy, and a reduction in active engagement. All of these things have an impact on student learning abilities. The largest distractions at home are considered among these disadvantages of distant education, particularly for subjects with low awareness. These distractions cause a movement of the student's attention from the current lesson to disturbing events. For this reason, there is a need to experiment with new solutions also linked to Information Technology (IT) to improve the focused learning during distance education. Our tool's technical idea is to create a real-time summary of the topic treated by the teacher. The system captures the text every five minutes, generates outlines, and browses them to eliminate repetitive portions after each survey. We looked at two different sorts of filters, semantic and summary, to see if the first could distinguish between topics and the second could evaluate the topic's highlights. Natural Language Processing algorithms are used to extract categories and keywords from the general generated summary. The latter will emphasize the most important points of the speech, while the keywords will be utilized to extract the candidate literature about the discussed topics.